Article Per Year (5 Year)
p-Index From 2019 - 2024
1.325
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Teknik Industri Journal of Mechanical Engineering Science and Technology Journal of Research and Technology Civil Engineering Dimension BENTANG : Jurnal Teoritis dan Terapan Bidang Rekayasa Sipil Jurnal Pengabdian, Pendidikan dan Teknologi International Journal of Industrial Engineering and Engineering Management
Aisyah Larasati, Aisyah
Unknown Affiliation
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation Other

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : BENTANG : Jurnal Teoritis dan Terapan Bidang Rekayasa Sipil

 1 
Simulasi Progres Proyek Konstruksi Time Performance Menggunakan Earned Value Management dengan Integrasi Artificial Neural Network: Time Performance Construction Project Simulation Using Earned Value Management with Artificial Neural Network Integration Setiyono, Setiyono; Hajji, Apif Miptahul; Larasati, Aisyah; Alfianto, Imam
Bentang : Jurnal Teoritis dan Terapan Bidang Rekayasa Sipil Vol 12 No 1 (2024): BENTANG Jurnal Teoritis dan Terapan Bidang Rekayasa Sipil (Januari 2024)
Publisher : Universitas Islam 45

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/bentang.v12i1.7296

Abstract

Digitization of construction using technologies such as AI, Big Data, Machine Learning, and Internet of Things (IoT) can help improve the productivity and efficiency of the construction industry. Machine learning methods such as Artificial Neural Network (ANN) are used to solve complex problems, including in construction projects. In addition, Earned Value Management (EVM) is used as a method to analyze and control project performance and estimate project completion time. Although EVM has the disadvantage of predicting estimated project completion times that are linear in nature, the use of non-linear methods such as Artificial Neural Network can help improve the accuracy of estimating project completion times that are complex and have a high degree of variation. This research aims to analyze the physical achievements of the work and predict estimates of work completion using Earned Value Management which is integrated with the Artificial Neural Network on the Advanced Development Project for Facilities and Infrastructure of the Sanggabuana Karawang Training Area Maintenance Detachment (Sarpras Denharrahlat) belonging to the Indonesian Ministry of Defense. By using EVM and ANN, project management can be improved in terms of cost control, scheduling, and better estimation of project completion time. The data analysis method used is EVM. The data analyzed is Week, Planned Value and Earned Value. The three data are then plotted and translated in graphical form to calculate the Schedule Variance and Schedule Performance Index to evaluate the project. After conducting an analysis using the EVM, then a predictive model is formed to calculate the estimated project completion time using the Artificial Neural Network (ANN) method. Next, optimization of the predictive model is carried out to reduce the level of prediction error. The Software used to build predictive models is RapidMiner. At week 26, work progress was 100% ahead of plan, with an Earned Schedule value of 31.00. This indicates that the project has been completed in accordance with the target time. Certain parameter modifications to the optimal model include training cycles of 100, learning rate of 0.316, and momentum of 0.316. With these parameter settings, the model produces the most accurate predictions with a low prediction error rate.
Co-Authors Abdul Muid Agus Rachmad Purnama Aminnudin, Aminnudin Budi Darmawan, Vertic Eridani Darmawan, Vertic Eridani Budi Fathullah, Muhammad Hajji, Apif Miptahul Imam Alfianto Indra, Sofiandi Dwi M. Hajji, Apif M. Hajji Mahsuli, Taufiq Maulana, Jibril Nugraha, Cahya P. Lewis, Michael Setiyono Setiyono Siregar, Ivana Maretha Syah, Yasifun Ardian Vania, Nur Izza Yue, Huang